Leveraging Natural Language Processing for Enhancing Sales Chatbots

With natural language processing (NLP) capabilities, sales representatives can drive engagement, personalize interactions and boost sales conversions. How can developers incorporate these techniques into their sales chatbots?

How Developers Can Use NLP to Improve Sales Chatbots

Many sales departments use artificial intelligence chatbots. According to McKinsey & Company, sales is the most common application of generative technology, with around 34% of respondents stating they regularly use it for this function.

Unlike rule-based chatbots, these AI chatbots are adaptive and highly responsive. Thanks to NLP, they can operate outside of a strict path, better engaging consumers. The ability to recognize the meaning behind words and phrases enables dynamic, believable interactions, improving overall sales effectiveness. 

Sales departments leveraging chatbots should adopt NLP techniques like sentiment analysis, tokenization or named entity recognition to drive engagement. They can reduce revenue leaks by 5% to 10% by improving engagement to mitigate churn risks. On top of cushioning their employers’ bottom lines, these gains support ongoing fine-tuning. 

With NLP functionality, AI chatbots can recognize mood, translate user input, help develop customer profiles or personalize interactions. Fast, custom interactions help companies identify consumers’ pain points and close sales faster, ultimately increasing conversions. 

NLP Techniques That Enable Complex Customer Inquiries 

These key NLP techniques can help chatbots understand the meaning, purpose and scope of complex inquiries sooner. 

Sentiment Analysis

With sentiment analysis capabilities, AI chatbots can categorize text as positive, negative or neutral. Sales representatives can use this technique to assuage negative emotions and deliver a more personalized experience, helping them connect with consumers better. 

To enable sentiment analysis, you must train an ML model to extract context from text in NLP applications. It needs prelabeled data to categorize moods or opinions accurately. 

In practice, text gains meaning based on preceding and subsequent strings of words. Tokenization reduces words and phrases to their root forms to extract semantic meaning. From there, the algorithm considers input context. 

Business Logic

Integrating business logic into traditional NLP capabilities enables higher-level analysis and decision-making. When an AI understands the rules behind a company’s sales processes, its output improves. 

Intent Recognition

Intent recognition enables an NLP model to identify the goal or purpose behind user input. Identifying clear purchasing intent helps sales representatives prioritize specific customers during inbound sales processes, increasing effectiveness. 

Training an algorithm to recognize consumers’ intentions is relatively straightforward — you simply provide examples of customer interactions alongside intent labels. With supervised learning, you can quickly train this NLP technique. 

Entity Extraction 

ML models capable of entity extraction — known as named entity recognition — can identify key elements like names, contact information and dates. 

Enabling this NLP technique involves manually creating a database of predefined named entities. The AI will quickly learn the rules for predicting which words are associated with which categories. 

In practice, entity extraction populates a database with context-specific data points, making higher-level analysis possible. You can analyze the relationship between entities, perform dynamic sentiment analyses or extract events. 

How to Fine-Tune NLP Capabilities Within Sales Chatbots 

If current trends continue, the global NLP market will become extremely profitable. For reference, experts project its revenue will reach an estimated $43.28 billion in 2025, up by roughly 250% from 2020. In other words, this technology is here to stay. 

Since investing in NLP capabilities for sales chatbots will remain essential, you must understand the intricacies of fine-tuning to keep performance consistent. Ongoing training is crucial to mitigate concept drift — a change in the relationship between the input and target output that invalidates previous training. 

Say your employer’s definition of buying intent changes or the sales department begins offering new features. In this concept drift scenario, your ML model will suddenly become less accurate.

Crucially, you must adjust model weights so older, less relevant data points are not considered as impactful as new information. 

You should also consider adopting a human-in-the-loop approach for fine-tuning. The person supporting the sales chatbots can iteratively refine prompts and provide critical feedback to your department, helping you identify and resolve pain points as soon as they appear. 

Improving Sales Chatbot Efficiency With NLP Capabilities 

Sales chatbots with NLP capabilities are more flexible and effective than their counterparts. However, they still need human oversight. With proper cleaning, transformation and training, you can ensure your organization’s AI technology is as robust as possible.